招聘匹配智能匹配算法Python代码示例 - 技能和经验匹配度计算

对于招聘匹配智能匹配算法，具体实现细节可能会有所不同，下面是一个简单的例子，用Python实现：\n\npython\n# 定义一个求余弦相似度的函数\ndef cosine_similarity(vec1, vec2):\n dot_product = sum(p*q for p,q in zip(vec1, vec2))\n magnitude = math.sqrt(sum([val**2 for val in vec1])) * math.sqrt(sum([val**2 for val in vec2]))\n if not magnitude:\n return 0\n return dot_product/magnitude\n\n# 定义一个函数，用于计算技能匹配程度\ndef skill_match(candidate_skills, job_skills):\n candidate_vector = [1 if skill in candidate_skills else 0 for skill in job_skills]\n return cosine_similarity(candidate_vector, [1]*len(job_skills))\n\n# 定义一个函数，用于计算经验匹配程度\ndef experience_match(candidate_experience, job_experience):\n return cosine_similarity(candidate_experience, job_experience)\n\n# 主函数\ndef main():\n # 候选人技能\n candidate_skills = ['Python', 'Java', 'C++', 'Data Analysis']\n # 候选人经验\n candidate_experience = [1, 0, 1, 1]\n \n # 招聘岗位技能要求\n job_skills = ['Python', 'Data Analysis', 'Machine Learning']\n # 招聘岗位经验要求\n job_experience = [1, 1, 0]\n \n # 计算技能匹配度\n skill_match_score = skill_match(candidate_skills, job_skills)\n print("技能匹配度：", skill_match_score)\n \n # 计算经验匹配度\n experience_match_score = experience_match(candidate_experience, job_experience)\n print("经验匹配度：", experience_match_score)\n\nif __name__ == "__main__":\n main()\n\n\n以上代码实现的是一个简单的招聘匹配算法，通过计算候选人的技能匹配度和经验匹配度来评估候选人和招聘岗位的匹配程度。具体的技能匹配度和经验匹配度的计算方式可以根据实际需求进行调整。